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Environmental Processes

Suspect screening and regulatory databases: A powerful combination to identify emerging micropollutants Pablo Gago-Ferrero, Agnes Krettek, Stellan Fischer, Karin Wiberg, and Lutz Ahrens Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b06598 • Publication Date (Web): 21 May 2018 Downloaded from http://pubs.acs.org on May 21, 2018

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Environmental Science & Technology

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ACS Paragon Plus Environment


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ABSTRACT

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This study demonstrates that regulatory databases combined with the latest advances

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in high resolution mass spectrometry (HRMS) can be efficiently used to prioritize and

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identify new, potentially hazardous pollutants being discharged into the aquatic

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environment. Of the approximately 23,000 chemicals registered in the database of the

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National Swedish Product Register, 160 potential organic micropollutants were

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prioritized through quantitative knowledge of market availability, quantity used,

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extent of use on the market, and predicted compartment-specific environmental

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exposure during usage. Advanced liquid chromatography (LC)-HRMS-based suspect

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screening strategies were used to search for the selected compounds in 24-hour

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composite samples collected from the effluent of three major wastewater treatment

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plants (WWTPs) in Sweden. In total, 36 tentative identifications were successfully

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achieved, mostly for substances not previously considered by environmental scientists.

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Of these substances, 23 were further confirmed with reference standards, showing the

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efficiency of combining a systematic prioritization strategy based on a regulatory

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database and a suspect screening approach. These findings show that close

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collaboration between scientists and regulatory authorities is a promising way forward

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for enhancing identification rates of emerging pollutants and expanding knowledge on

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the occurrence of potentially hazardous substances in the environment.

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INTRODUCTION

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The world of chemicals is very complex, with more than 100,000 substances in commercial use

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globally. Emissions of synthetic substances into the aquatic environment pose a risk to water

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quality and may trigger unwanted effects.1 The majority of regulatory and enforcement

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agencies responsible for water quality assume that a small number of well-known substances

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(e.g., priority pollutants described by the EU Water Framework Directive) are responsible for a

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significant share of environmental, human health, and economic risks.2 However, the accuracy

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of this assumption is questionable, since the chemicals which are regulated by official agencies

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represent only a tiny fraction of the chemical stressors occurring in the environment. Most

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potentially hazardous compounds are thus not covered by any existing water quality

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regulations or included in environmental screening programs. Chemical monitoring and

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analysis are commonly carried out for a pre-selected small proportion of organic

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contaminants, thus overlooking important site-specific and potentially hazardous substances.3

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This is particularly the case with many industrial chemicals, which are systematically omitted

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from monitoring studies.

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The latest advances in high resolution mass spectrometry (HRMS) have initiated a new trend in

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analytical data processing in recent years. Targeted analytical methods are now often

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complemented with suspect and non-target data acquisition and screening methods.4,5 In

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suspect screening workflows, there is no need for reference standards until the confirmation

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stage, thus saving time and money and allowing the inclusion of an extensive list of

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substances.

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Suspect screening is a complex process, and a crucial step within this is to produce smart

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suspect lists in order to achieve a better understanding of specific research questions.

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Different strategies for selection of suspects in environmental samples have been developed

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for various chemicals and compound classes, e.g., for registered pesticides and associated



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transformation products (TPs)6,7, pharmaceuticals8–10, their predicted metabolites11–13 and

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photodegradation products14–18, surfactants11,14,19–22, fracking fluids23, predicted TPs24–28, new

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psychoactive substances29, and illegal additives,30 as well as for different families of

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compounds31–40.

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Ideally, when creating suspect screening lists, all pre-existing relevant information is

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considered. The Swedish chemicals legislation requires manufacturers and importers to

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register chemical substances and products in a national product register. Chemical registration

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data show the identity of substances released on the market and contain information on e.g.,

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market availability, use pattern, and quantity used, which are basic facts required for

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predicting the extent of their use on the market and the risk of uncontrolled release during

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use. This information can be extremely valuable in selecting compounds to focus upon in

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suspect identification efforts, based on the chances of them being present in the environment,

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and in finding new pollutants that are currently off the radar of environmental chemists. Most

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of these data are handled as confidential business information and cannot be found in the

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open literature. However, in Sweden the Chemicals Agency (KemI) has transformed the

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confidential data through aggregation and categorization of information into general exposure

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indices for different target groups. These exposure index tables were created in 2005 and have

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thereafter been updated annually. Nowadays, they are amongst others used for regulatory

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prioritization of selecting chemicals for national monitoring programs.

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In this study, we tested the hypothesis that regulatory databases are a powerful tool for

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prioritization of chemical substances and increase the chances of identifying emerging

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environmental pollutants, i.e., chemicals of environmental concern that have so far received

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little or no attention. The aim of the study was to use the latest advances in HRMS and the

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information contained in regulatory databases to detect and identify emerging pollutants in


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effluents from wastewater treatment plants (WWTPs). Our intention was to help create a

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broader picture of the presence of emerging pollutants in the environment.

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MATERIALS AND METHODS

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Chemicals, sampling, and analysis

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In order to characterize the wastewater in terms of well-known micropollutants, 74 substances

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were analyzed including pharmaceuticals and personal care products (PPCPs), per- and

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polyfluoroalkyl substances (PFASs), pesticides and artificial sweeteners. At a later stage, an

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additional 27 substances were purchased and analyzed, in order to confirm the preliminary

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suspect identifications. Full details about all chemicals analyzed are given in the Supporting

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Information (SI).

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Effluent samples were taken from three large-scale WWTPs located in Sweden: Uppsala

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(172,000 population equivalents (PE)), Stockholm (780,000 PE), and Västerås (120,000 PE),

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together covering >10% of the wastewater generated by the Swedish population. The

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wastewater treatment steps at these plants include mechanical treatment and primary

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sedimentation, biological treatment using activated sewage sludge with nitrogen removal,

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chemical treatment by addition of iron chloride, and lamella sedimentation to remove

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particulate matter. The selected WWTPs receive wastewater from both domestic and

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industrial settings and were representative examples for our purposes, i.e., to test whether

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regulatory databases can assist in prioritization of environmentally relevant substances. 24-

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hour composite samples of effluent were collected in February 2017, in pre-cleaned high-

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density polyethylene (HDPE) bottles. All samples were filtered through glass fiber filters (pore

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size 0.7 μm) and stored in darkness at -18 °C until analysis.



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Sample extraction was carried out in quintuplicate using the protocol previously described by

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Gago-Ferrero et al. (2015).11 In brief, solid phase extraction (SPE) was conducted for 200 mL of

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sample using four different SPE materials simultaneously (Oasis HLB, Isolute ENV+, Strata-X-

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AW, and Strata-X-CV) in an in-house cartridge to achieve sufficient enrichment for a broad

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range of compounds. In order to perform the screening, samples were further analyzed using

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an ultra-high performance liquid chromatography (UHPLC) system coupled to a quadrupole-

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time-of-flight (QTOF) mass spectrometer (UHPLC-HRMS, G2S Xevo, Waters) (for details see SI).

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Quality assurance and quality control information is provided in SI.

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Targeted analysis of the 74 micropollutants (Table SI-1 in SI) was carried out following the

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validated method previously described by Gago-Ferrero et al. (2017).41 The suspect screening

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was performed as outlined below. Details about the quality assurance and quality control can

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be found in the supporting information text and elsewhere.41

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Prioritization based on regulatory database

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The National Swedish Product Register (supervised by the Swedish Chemical Agency (KemI))

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was used in order to prioritize chemicals. This register database contains information on all

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chemical products released on the Swedish market at volumes of at least 100 kg per year per

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manufacturer or importer. End-products of foodstuffs, cosmetics, medicines, and hygiene

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products are not included in the register. However, the raw materials for these have to be

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registered if they occur on the Swedish market. The database contains data reported since

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1992 and comprises ~23,000 chemicals, of which around 14,000 are present in active products

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(2015). Use of a consistent suspect screening approach for such an extensive list of substances

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is not feasible, because only some parts of the data evaluation process can be completely

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automatized, while others need to be carefully revised by experts in order to communicate the

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identifications with an acceptable level of confidence, e.g., final evaluation of the MS/MS


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spectra.4 Therefore, smart prioritization is required in order to reduce the number of suspects

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with a high chance of being present.

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As discussed earlier, most information listed in national registers is confidential. However,

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aggregation and categorization of the original data can in many cases transform them into

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non-confidential (but still useful) information. One example is the Exposure Index (EI)42

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developed by KemI. EI values are calculated for all substances in the database that are in use in

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Sweden. The EI is an indicator for the possibility of a substance to expose a primary recipient,

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based on the total use pattern of the chemical in Sweden, while the environmental fate of the

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substance is not considered. Each chemical is allocated a single value between 0 (low) and 7

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(high) that considers product tonnages, occurrence on the market, and dispersion to specific

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recipients of the chemical from end-products (e.g., goods and materials) that occurs during

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usage. The EI thus reflects the use and emission pattern and is recipient-specific (has different

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indices for e.g., soil, air, surface water, sewage water). The calculation of the EI is done in

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several steps. First, a product specific exposure index (EIprod) is calculated for each specific

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chemical in each product in the register database42. EIprod describes the general potential for a

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substance to be released from a specific product use and for its calculation the following data

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are used: (i) the function of the product; (ii) the product specific sector of use; and (iii) the

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annual tonnage of the substance in the product. The registrant has to select predefined

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functions and the sector of use. Each function and sector of use have a predefined exposure

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estimation, based on expert judgments.43 All EIprod for a substance are then added together

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(quantity weighted) into an overall “Exposure Index” (normalized into a scale of 0-7).

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As the objective of this study was to analyze WWTP effluent, we focused solely on compounds

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with EIsewage-water values >5. Inorganic salts were also excluded, as were substances with log KOW

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>10 to ensure the amenability in LC-HRMS detection and to exclude extremely hydrophobic

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chemicals that are readily removed in WWTPs (by sedimentation and flocculation). Other



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works have considered chemicals with log KOW values ≤5 to be relevant water pollutants.44 We

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opted for a higher log KOW threshold since: i) approximately one-third of substances in the

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database with an EIsewage-water value between 5 and 7 had a log KOW between 5 and 10, ii) it has

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been demonstrated that treatments applied in conventional WWTPs lead to the release of

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substances with log KOW >511,19, and iii) several compounds with log KOW between 5 and 10 have

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previously been detected in WWTP effluent samples.11,45 In a last step, compounds that were

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not ionizable with electrospray in LC-HRMS analysis (based on expert knowledge) were also

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excluded. Finally, of the initial 23,000 compounds, 160 were selected as suspects (Figure 1,

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Table SI-2 in SI). The hypothesis tested was that these substances have a high possibility of

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being present in wastewater, although for most there is a lack of information in the literature

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on their occurrence in the aquatic environment.

Figure 1: Prioritization workflow for creation of our suspect screening list.

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Suspect screening performance

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Tentative identification of the suspects was carried out following the workflow previously

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described by Gago-Ferrero et al. (2015).11 In brief, the criteria used for reduction of features in

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both ionization modes (ESI(+) and ESI(-)) include: i) a threshold in peak area (≥200 for ESI(+)

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and ≥100 for ESI(-)), ii) a threshold in intensity counts (≥100 for ESI(+) and ≥50 for ESI(-), which

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is roughly equivalent to a signal to noise ratio of 10), iii) a threshold in mass accuracy of ±2

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mDa on the monoisotopic peaks, iv) a good isotopic pattern fit, and v) chromatographic


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retention time (Rt) plausibility using a quantitative structure-retention relationships (QSRR)

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prediction model.46 Additional evidence to support the identifications was obtained based on

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in-depth scrutiny of the MS/MS spectra by comparing with spectral libraries (European

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MassBank)47, using in silico fragmentation software (MetFrag48 and CFM-ID49), and expert

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knowledge. For tentatively identified substances that were commercially available, the

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reference substances were purchased in order to confirm the identity. The concentrations of

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the confirmed compounds in the samples (semi-quantitative analysis) were determined by

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standard addition (by considering the recoveries calculated afterwards).

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The level of confidence in identification of the detected compounds was communicated

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according to the system described by Schymanski et al. (2014)50, where Level 1 corresponds to

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confirmed structures (with reference standard), Level 2a to probable structures by library

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spectrum match, Level 2b to probable structures by diagnostic evidence, Level 3 to tentative

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candidate(s), Level 4 to unequivocal molecular formulae, and Level 5 to exact mass(es) of

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interest.

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RESULTS AND DISCUSION

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Characterization of effluent samples in terms of well-known micropollutants

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In total, 58 of the 74 target substances were detected at least once and 47 compounds were

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detected in all samples, demonstrating the ubiquitous presence of xenobiotics (Table SI-3 in

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SI). Some compounds were found at particularly high concentrations, including metoprolol (up

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to 1800 ng L−1), metformin (up to 1400 ng L−1), losartan (up to 670 ng L−1), tramadol (up to 510

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ng L−1), furosemide (up to 470 ng L−1), caffeine (up to 410 ng L−1), atenolol (up to 370 ng L−1),

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and diclofenac (up to 290 ng L−1), among others. These levels are within the range reported in

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previous studies analyzing treated wastewater in the study region51,52 and also in other regions



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of Europe.53 Therefore, the samples analyzed in this study were representative WWTP effluent

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samples to test the hypothesis that regulatory databases can be an efficient tool for prioritizing

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compounds that are little studied, but have a high chance of being discharged to the

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environment.

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Identification of the prioritized suspects

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Screening for the prioritized chemicals (the suspects) in the effluent samples at the selected

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locations yielded 27 hits using ESI(+) and 46 using ESI(-) mode when applying the previously

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described thresholds of ion intensity, peak area, mass accuracy, isotopic fit, and

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chromatographic Rt (see Materials and Methods). There was an overlap of 10 compounds

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detected in both modes. The Rt prediction model reduced the number of hits by 25%, showing

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that the use of a reliable Rt prediction model increases the chances of high accuracy and saves

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time and effort.

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Since a multitude of compounds (from one to several thousands) can share a given molecular

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formula, additional investigations of the MS/MS spectra (using available spectral libraries

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(MassBank) and in silico fragmentation prediction tools (MetFrag) were performed in order to

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reach tentative identifications.4 For compounds at Level 4 (unequivocal molecular formula) or

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5 (exact mass of interest) for which no additional evidence could be found (Table SI-4 in the

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SI), no further investigation was conducted within this study. Following this workflow, the 73

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hits were reduced to 40 tentatively identified compounds (Level 2 and 3). In the last step,

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available reference standards were purchased. After comparing Rt and MS/MS spectra with

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the reference compounds, four more compounds were rejected (benzylamine, dazomet,

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diethyl thiourea, and natamycin). Table 1 summarizes the 36 compounds confirmed with

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standard (n = 23) or tentatively identified (n = 13) with their corresponding level of confidence,

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Rt, detected adducts, the additional evidence that led to their identification, and information

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related to their consumption, their area of application, and semi-quantitative values obtained


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for the confirmed compounds. In addition, the MS/MS spectra have been included in the SI

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(Figure SI-2) for compounds for which this information is not available in Massbank.

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The identification methodology can be exemplified for the case of 2,2-dimorpholinyldiethyl-

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ether (Figure 2). The chromatographic peak associated with this substance met all threshold

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conditions applied in the feature reduction steps, including a plausible Rt according to the

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QSRR model.46 These facts made this suspect a suitable candidate for further investigation.

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MS/MS spectra for this substance were not found in the MassBank spectral database.

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However, the fragments at m/z 158.118, 114.0919, 112.0757, 86.0964, 84.0801, and 70.0651

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fit very well with the investigated substance, corresponding to [C8H16NO2], [C6H12NO],

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[C6H10NO], [C5H12N], [C5H10N], and [C4H8N], respectively. It also provided the highest MetFrag

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score using the Chemspider database. Therefore, this substance was considered tentatively

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identified at Level 2b (and would have remained at this level if the corresponding standard was

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not available). However, the reference standard was purchased, and the identification was

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confirmed by MS/MS and Rt comparison, reaching Level 1. The concentrations in the samples

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analyzed were in the range 14-24 ng L-1. The compound 2,2-dimorpholinyldiethyl-ether is used

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as sealant and adhesive, mainly in industry and, to the best of our knowledge, has not been

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detected previously in water samples.

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It was possible to use data from the spectral library MassBank in several cases, increasing the

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confidence in the identifications. One such case was tris(2-butoxyethyl) phosphate,

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summarized in Figure S1 in SI. The fragments at m/z 98.9837, 199.071,4 and 299.1607 are

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characteristic for that substance, corresponding to [H4O4P], [C6H16O5P], and [C12H28O6P],

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respectively. They fit very well with the MassBank spectrum record SM880602, leading to Level

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2a identification. Finally, this compound was confirmed with a reference standard. MassBank

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was also used in the identification of sebacic acid, 1,2,3-benzotriazole, sulisobenzone, stearic

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acid, 2-(dodecyloxy)ethyl hydrogen sulfate, acesulfame, pyridoxine, and panthenol.



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In other cases, finding a plausible Rt between different substances belonging to a given

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homologous series was also used as additional evidence. This was the case for alkyl sulfates

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(AS). Laurilsulfate (C12-AS) was confirmed with a reference standard. For the rest of the ASs

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(C8-AS – C16-AS), it was observed that their Rt increased constantly with number of carbons,

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and the MS/MS spectra showed the same characteristic fragments (m/z 79.9568 [O3S] and

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96.9596 [HO4S]). Those AS compounds were tentatively identified at Level 3, although

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evidences for a possible structure exist, there is insufficient information for the exact structure

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(i.e. positional isomers). A similar observation was made for the series of alkyl ethoxy sulfates

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(AES), where C12-AE1S was tentatively identified at Level 2a and C12-AE2S, C12-AE3S, C12-

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AE4S, and C13-AE3S at Level 3.

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In total, of the 160 prioritized compounds, 40 were tentatively identified and 23 were finally

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confirmed with a reference standard. Only four compounds were rejected on checking their

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identity against the reference standard, which demonstrates the suitability of the suspect

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screening approach and the prioritization strategy (see the following sections) for our

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purposes.


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(a) Intensity [Count]

Accurate Mass = 245.1861 Mass Error = 0.1 mDa Experimental Rt = 0.82

Retention time [min]

Intensity [Count]

(b)

Observed mass [m/z]

Non-spiked sample

Spiked with 0.5 µg absolute

Spiked with 1 µg absolute

Intensity [Count]

(c)

Retention time [min]

Figure 2: Identification of the compound 2,2-dimorpholinyldiethyl-ether: (a) full MS chromatogram for the corresponding mass (±2.5 mDa) (b) MS/MS spectra and corresponding fragments, and (c) confirmation step using standard addition.



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Environmental relevance of the identified suspects

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Non-confidential industrial category and number and type of products in which each identified

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compound has applications, along with the respective annual production volume and

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concentration ranges (semi-quantitative data), are summarized in Table 1. In addition, toxicity

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values for the tentatively identified and confirmed suspect compounds were estimated, based

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on the ecological structure-activity relationships (ECOSAR) predictive model (Table SI-5 in SI).54

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Recipient, reference unit, threshold levels and levels of concern are summarized for tentatively

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identified and confirmed compounds

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A few of the identified compounds are widely described in the literature as common

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micropollutants. These include the artificial sweetener acesulfame, the food preservative

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benzoic acid, the UV filter sulisobenzone, and the corrosion inhibitor 1,2,3-benzotriazole.

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Acesulfame showed some of the highest concentrations of all confirmed compounds, reaching

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levels above 15000 ng L-1. This is in agreement with previous findings that acesulfame is one of

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the most abundant substances in wastewater and surface water, due to its high consumption

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in urban areas.11 Sulisobenzone, benzoic acid, and 1,2,3-benzotriazole exhibited equally high

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levels, reaching concentrations above 1000 ng L-1 which is comparable to the level reported in

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other studies. The aquatic toxicity according to the ECOSAR prediction model was moderate

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(i.e., acesulfame, benzoic acid) to high (i.e., 1,2,3-benzotriazole, sulisobenzone). The endocrine

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disruptive effects described for sulisobenzone55 and its widespread occurrence in surface

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waters 11,56,57 make it a candidate for inclusion in monitoring programs.

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Several sulfonate anionic surfactants were identified. Regarding the AS, compounds between

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C8-AS and C16-AS were detected and the confirmed substance laurilsulfate (C12-AS) occurred

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in concentrations ranging from 660 ng L-1 to 1800 ng L-1. The other AS could not be confirmed

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due to lack of commercial standards, but their chromatographic peak intensity was equal to or

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higher than that of C12-AS, suggesting a similar range of concentration. Alkyl sulfates are


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widely used in the formulation of cleaning products and are prioritized for further

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ecotoxicological assessment using real tests, according to ECOSAR. These compounds can also

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be TPs from AES.58 Five additional AES were tentatively identified (i.e. C12-AE1S, C12-AE2S,

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C12-AE3S, C12-AE4S, and C13-AE3S) showing peaks with large intensity. These surfactants are

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used as raw materials for cosmetics and are classified as a moderate aquatic toxicity concern.

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Although these substances show good removal efficiencies in WWTPs, AES are often TPs of

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other, more complex surfactants and release to the environment is probable, with unknown

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effects.59 Far more alarming, however, is the surfactant 4-dodecylbenzesesulfonic acid (widely

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used in painting and cleaning products, among others), which was detected at very high levels

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(5800-22400 ng L-1) and is estimated to be ecotoxicologically relevant.60 Other non-sulfonated

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surfactants identified were tetraethyleneglycol and laureth 5 (used in a variety of products

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related to the paint industry and cosmetics, respectively) and oleic acid, stearic acid, and

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sebacic acid (widely used in the manufacture of detergents, soaps, and cosmetics, or as

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plasticizers). These substances were confirmed in all samples. High levels were determined for

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all of these, but particularly for sebacic acid (41000-230000 ng L-1), which exceeded the highest

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ever level reported in the literature for this compound in water samples. Ricinoleic acid was

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determined at levels up to 6500 ng L-1 and, to the best of our knowledge, this is the first

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evidence of its presence in wastewater. However, some of these substances (oleic acid, stearic

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acid, and sebacic acid) also show high natural occurrence and are not environmentally toxic.54

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The situation appears different for the five identified organophosphate compounds, namely di-

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(2-ethylhexyl)phosphoric acid, dibutyl phosphate, diethyl hexyl phosphate, mono-n-

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butylphosphoric acid, and tris(2-butoxyethyl) phosphate. All of these have high predicted

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toxicity values and, in some cases, this toxicity has been experimentally demonstrated.61–66

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Tris(2-butoxylethyl) phosphate is widely used in the painting industry and it was detected at

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concentrations of 17 ng L-1 to 2200 ng L-1, as also reported in previous studies.38,67,68 The other

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organophosphates, exclusively consisting of phosphoric acid di- and monoesters, have been



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less described in the literature.66,69–72 Concentrations of the here detected organophosphates

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(excluding tris(2-butoxylethyl) phosphate) were detected in the range 14-360 ng L-1 and are

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remarkably higher than those previously reported.73 This might be explained by high loads in

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the sewage or microbial hydrolysis of phosphoric acid triesters to the corresponding diesters.73

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Considering their high use, high toxicity, and high levels found in WWTP effluent (up to 750 ng

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L-1), more attention is needed regarding the presence of these compounds, mainly used as

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plasticizers, in the environment.

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For the first time, to the best of our knowledge, the presence of triisopropanolamine

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(concentration range between 6-21 ng L-1) was reported in environmental samples. This

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substance is used as an emulsifier or stabilizer in various industrial applications. Other

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compounds whose presence was reported for the first time in environmental samples were N-

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butyldiethanolamine (21-78 ng L-1) and N,N-dimethoyl-1-tetradecanamine (12-72 ng L-1),

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mainly used in the metal and textile industry, respectively. All three of these compounds are

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toxicologically relevant according to the ECOSAR model. Hence, more studies focusing on the

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distribution, fate, and toxicity of these chemicals are needed in order to evaluate their

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potential risks to the environment. Other noteworthy substances that were identified in the

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present study were panthenol (41-340 ng L-1), pyridoxine (5-53 ng L-1), and nicotinamide (32-91

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ng L-1), which are used as raw materials in cosmetics and hygiene articles and as food additives.

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Efficiency of the prioritization strategy based on regulatory databases in the

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selection of suspects

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The results in this study demonstrated that regulatory databases are a powerful tool in the

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selection of chemicals to monitor. 36 compounds were identified (23 confirmed) from a

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suspect screening list of 160 compounds. It is noteworthy that this list included mainly


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uninvestigated or only partly investigated substances. These facts confirm the good

336

performance of the prioritization approach (and the suspect screening approach).

337

Several prioritization approaches for organic pollutants are described in the literature, most of

338

which compare modeled or measured occurrence concentrations and/or toxicological

339

impacts.74 The majority of these approaches focus on the occurrence in surface waters by

340

assessing monitoring data,65,75,76 and on the establishment of toxicity rankings for already

341

known suspect compounds. This is understandable in view of the wide range of prioritization

342

methods that have been proposed for pharmaceuticals.77–79 However, these strategies are

343

biased since only substances that have previously been found in other studies are considered,

344

which creates a loop whereby most studies focus on the same compounds. Meanwhile, a large

345

number of potentially hazardous compounds for the environment remain hidden. As the

346

results of the present study show, the use of market data from regulatory databases allows the

347

range of substances of interest to be extended, to include e.g., many industrial compounds

348

that can easily reach the environment and are rarely studied by environmental chemists.

349

Studies using market data for prioritization purposes are very scarce. Chiaia-Hernandez et al.34

350

compiled a suspect list based on consumption data (including insecticides, fungicides, biocides,

351

acaricides, pharmaceuticals, and metabolites) and confirmed the presence of three relevant

352

substances in sediments. Other interesting studies have used national databases to screen for

353

pesticides (and TPs) and have identified >100 pesticides and TPs, some of them for the first

354

time, showing the good performance of those approaches.6,39 Another study has investigated

355

compounds authorized on the market considering European regulatory frameworks under

356

REACH.80 However, only very well-known compounds could be identified (e.g., caffeine or

357

tramadol), since MS/MS data were not considered for the identifications.

358

Prioritization based on the Swedish EI proved to be a good strategy in order to obtain relevant

359

lists of compounds with a significant risk of being present in the Swedish environment. As



360

mentioned above, of the 160 selected compounds, 36 were identified. A few of the identified

361

compounds have been commonly detected previously in environmental studies (e.g.,

362

acesulfame or sulisobenzone).11,55–57 However, to the best of the authors knowledge, most

363

identified substances have been rarely studied or not at all (e.g. 2,2’-dimorpholinyldiethyl-

364

ether). This clearly shows one of the main advantages of our prioritization strategy, that the

365

prioritization list obtained is not biased by previous studies focusing on the occurrence of

366

micropollutants. The use of market data did significantly increase the chances of identification,

367

since it provided solid evidence that those substances were being actually used in the study

368

area. Therefore, the use of market data may be a valid indicator that helps to obtain a broader

369

picture regarding the presence of micropollutants in the environment. Limitations may arise

370

where countries do not keep or maintain a comprehensive market data register as the one in

371

Sweden.81 The use of other additional technical criteria to ensure accuracy in analysis of the

372

selected compounds is of paramount importance to obtain high percentage identification rates

373

and be time efficient.

374

The ratio of tentative identifications in relation to the hits obtained after applying the

375

reduction of feature thresholds was 49% (40% in ESI(+) and 55% in ESI(-)). In most studies

376

dealing with suspect screening, a much higher percentage identification (however not

377

necessarily in absolute numbers) is achieved in ESI(-) than in ESI(+)19,44, because: i) a much

378

larger number of compounds ionize well in ESI(+) in comparison with ESI(-), ii) negative

379

compounds tend to show more characteristic fragments (e.g., [O3S], [HO4S]) that facilitate their

380

identification, and iii) the noise in the chromatograms is lower in ESI(-). The fact that the rate

381

of identification in the two modes was almost equal in the present study can be explained by

382

good pre-selection of compounds and the high number of confirmations carried out with

383

reference standards.


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Page 19 of 41


384

The results in this study clearly show that close collaboration between scientists and

385

regulatory authorities is a very promising way to enhance identification rates and advance

386

knowledge on the occurrence of potentially hazardous substances that are dispersed in the

387

environment. In contrast to previous studies, this work provides semi-quantitative information

388

for all confirmed compounds. Achieving a better understanding of the levels of pollutants in

389

WWTP effluent allows sound evaluation of the potential need to monitor these compounds in

390

future studies, in e.g., surface or drinking water. This study did not consider toxicity data in the

391

prioritization approach, since the aim was to test the efficiency of a prioritization strategy

392

solely considering a market-based governmental databank. The vast majority of the

393

compounds identified are toxicologically relevant (Table SI-4 in the SI). However, some

394

substances with low or unknown toxicity were also detected (e.g., stearic acid or

395

tetraethyleneglycol). In future prioritization strategies, it would be useful to include toxicity as

396

an additional prioritization factor, in order to focus exclusively on the identification of

397

hazardous emerging pollutants.

398

Acknowledgments

399

This study was supported by the Swedish Research Council Formas through the project RedMic

400

(216-2012-2101).

401

Supporting information

402

Chemical and reagents, instrumental analysis, prioritization, quality assurance and quality

403

control, target analysis of micropollutants, identification of suspects, toxicity of the identified

404

suspects

405

406

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Sanderson, H.; Johnson, D. J.; Reitsma, T.; Brain, R. A.; Wilson, C. J.; Solomon, K. R. Ranking and prioritization of environmental risks of pharmaceuticals in surface waters.



Regul. Toxicol. Pharmacol. 2004, 39 (2), 158–183.

661 662 663 664

(80)

Sjerps, R. M. A.; Vughs, D.; van Leerdam, J. A.; ter Laak, T. L.; van Wezel, A. P. Datadriven prioritization of chemicals for various water types using suspect screening LCHRMS. Water Res. 2016, 93, 254–264.

665 666 667

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Lara-Martń , P. A.; Li, X.; Bopp, R. F.; Brownawell, B. J. Occurrence of alkyltrimethylammonium compounds in urban estuarine sediments: Behentrimonium as a new emerging contaminant. Environ. Sci. Technol. 2010, 44 (19), 7569–7575.


Page 26 of 41

Page 27 of 41


Table 1: Details on the 36 tentatively identified and confirmed suspect analytes including additional evidences that lead to their identification, information related to their consumption and use and semi-quantitative values (for confirmed compounds). a

Suspect analyte

Sebacic acid

Rt (min)

Additional Evidences for the identification

0.78

Presence of characteristic fragments m/z: 99.0445 [C5H7O2]; 109.0655 [C7H9O]; 165.0916 [C10H13O2] Similarity with MassBank [record PR100605] • CONFIRMED with ref. standard

Product quantityb

Type of products/ industrial categoryb

287 tonnes

• Antifreeze • Coolants and lubricants for metal shearing and metal forming • Grinding fluids • Heat transferring agents

116 products

Level of confidencec

Conc. range [ng/L ] (n = 3)

1

57-110

1

1200-16000

• Maintenance and repair garages for motor vehicles • Sale, maintenance and repair for motor vehicles • Industry for fabricated metal products • Electricity, gas, steam and air conditioning supply

C10H18O4 [M-H]0.83

Acesulfame

C4H5NO4S [M-H]

Presence of characteristic fragments m/z: 82.0293 [C4H4NO] Similarity with MassBank [record EA275659] CONFIRMED with ref. standard

16 tonnes

• Food additive • Sweetener

6 products

• Food industry

-



Benzoic acid

0.87

Presence of the fragment m/z: 77.0397 [C6H5] CONFIRMED with ref. standard

33 tonnes 186 products

C7H6O2 [M-H]Dibutyl phosphate

C8H19O4P [M-H]

4.38

Presence of characteristic fragments m/z: 78.9583 [O3P]; 96.9691 [H2O4P]; 153.0317 [C4H10O4P] CONFIRMED with ref. standard

1 tonne

Presence of characteristic fragments m/z: 79.9568 [O3S]; 210.0321 [C13H6O3]; 228.9809 [C8H5O6S] Similarity with MassBank [record TUE00147] CONFIRMED with ref. standard

0.4 tonnes

49 products

• • • •

In-car preservatives Binders for paints, adhesives Stabilizers Biocides for human hygiene

• • • • •

Perfumes and toiletries Paint industry Industry for cleaning and polishing preparations Industry for pulp, paper and paper products Maintenance and repair garages for motor vehicles

• Sealant • Brake fluid

Page 28 of 41

1

550-1300

1

76-370

1

120-1100

• Export • Industry for cleaning and polishing preparations • Sales establishment for motor vehicle parts and accessories • Construction of buildings

-

Sulisobenzone

4.63

21 products

• Raw material for cosmetics and hygienic articles • Washing-up liquids • Retail sale, except for such with motor vehicles

C14H12O6S [M-H]-


Page 29 of 41

Mono-n-butylphosphoric acid


4.76

Presence of characteristic fragments m/z: 78.9583 [O3P]; 96.9691 [H2O4P]; 137.0004 [C3H6O4P] • CONFIRMED with ref. standard

• Brake fluid

1 tonne 52 products

1

72-250

• Coolants and lubricants for metal handling

1

2600-6600

• Gear oil • Lubricant additives

1

14-150

• Export • Industry for cleaning and polishing preparations • Sales establishment for motor vehicle parts and accessories

C4H11O4P [M-H]Ricinoleic acid

8.70

Presence of characteristic fragment m/z: 279.2324 [C18H31O2]; 183.1385 [C11H19O2] CONFIRMED with ref. standard

0.8 tonnes 5 products

C18H34O3 [M-H]Di-(2-ethylhexyl)phosphoric acid

9.38

Presence of characteristic fragments m/z: 78.9584 [O3P]; 123.9923 [C2H5O4P]; 209.0945 [C8H18O4P] CONFIRMED with ref. standard


• • • • •

Industry for machinery and equipment Export Surface treatment and coating for metals Industry for motor vehicles Industry for coke, refined petroleum products

C16H35O4P [M-H]-



Laurilsulfate (C12-AS)

9.27

Presence of characteristic fragments m/z: 79.9567 [O3S]; 96.9688 [HO4S]; 136.9905 [C3H5O4S] • CONFIRMED with ref. standard

0.3 tonnes

Presence of characteristic fragments m/z: 183.01196 [C8H7O3S]; 198.0357 [C9H10O3S]; 79.9560 [O3S]; 170.0037 [C7H6O3S]; 197.0271 [C9H9O3S] Plausible MS/MS spectra also in PI Plausible Rt in PI (12.49) according to the QSRR model CONFIRMED with ref. standard

88 tonnes

• Cleaning products

Page 30 of 41

1

660-1800

1

5800-22000

1

210-600

6 products

C12H26O4S [M-H]2-Dodecylbenzenesulfonic acid

C18H30O3S [M-H] Oleic acid

C18H34O2 [M-H]

10.77/ 10.9

Presence of characteristic fragment m/z: 263.2379 [C18H31O] CONFIRMED with ref. standard Similarity with MassBank [record MT000029]

Car shampoo Paint, other solvent based for industry use Catalysts Cleaner Foam cleaner

• Production of other chemical products but synthetic fibres • Export • Sale, maintenance and repair of motor vehicles • Surface treatment and coating of metals • Paint industry

-

12.51

-

147 products

• • • • •


• • • •

Surface active agents Detergents Lubricant additives Coolants and lubricants for metal shearing

• Paint industry • Surface treatment and coating of metals • Production of other chemical products but synthetic fibres • Publishers and printers • Export


Page 31 of 41


Stearic acid

12.53

Presence of characteristic fragments m/z: 83.0494 [C5H7O]; 255.2317 [C16H31O2]; 265.2535 [C18H33O] Similarity with MassBank [record MT000015] CONFIRMED with ref. standard


C8H18O4S [M-H]

-

Nonyl hydrogen sulfate (C9-AS)

C9H20O4S [M-H]

6.08

-

6.90

Presence of characteristic fragments m/z: 79.9599 [O3S]; 96.9596 [HO4S] Best match with MetFrag RT is consistent among the analogue series RT and MSMS spectra plausible with the confirm compound Laurilsulfate Presence of characteristic fragments m/z: 79.9599 [O3S]; 96.9596 [HO4S] Best match with MetFrag RT is consistent among the analogue series RT and MSMS spectra plausible with the confirm compound Laurilsulfate

1

Export Industry for pulp, paper and paper products Industry for organic basic chemicals Production of other chemical products but synthetic fibres • Industry for plastic and rubber products • • • •

C18H36O2 [M-H]Octyl hydrogen sulfate (C8-AS)

• Raw material, intermediates that are not mentioned elsewhere • Raw material for cosmetics and hygienic articles • Material insulating from electricity • Lubricants • Putty

33 tonnes

• Surfactant in fire-fighting foam • Cleaning products

3

• Surfactant in fire-fighting foam • Cleaning products

3

3 products



41000-230000


Decyl hydrogen sulfate (C10-AS)

7.60

Presence of characteristic fragments m/z: 79.9599 [O3S]; 96.9596 [HO4S] Best match with MetFrag RT is consistent among the analogue series RT and MSMS spectra plausible with the confirm compound Laurilsulfate

33 tonnes

Page 32 of 41

Surfactant in fire-fighting foam Cleaning products

3

• Cleaning/washing agent for dish washing

3


3

3 products

C10H22O4S [M-H]Tridecyl hydrogen sulfate (C13-AS)

9.94



C13H28O4S [M-H]Tetradecyl hydrogen sulfate (C14AS)

C14H30O4S [M-H]-

10.9




Page 33 of 41


Pentadecyl hydrogen sulfate (C15AS)

11.7

C15H32O4S [M-H]Hexadecyl hydrogen sulfate (C16AS)

C16H34O4S [M-H]


2.2 tonnes


3

• Cleaning/washing

3

• Raw material for cosmetics

2a

5 products


-

2-(Dodecyloxy)ethyl hydrogen sulfate (C12-AE1S)

C14H30O5S [M-H]

12.16


-

10.33

Presence of characteristic fragments m/z: 79.9568 [O3S]; 95.9517 [O4S]; 96.9590 [HO4S]; 122.9752 [C2H3O4S]; 138.9701 [C2H3O5S]; 183.1749 [C12H23O] Similarity with MassBank [record ETS00008] RT is consistent among the analogue series Good match with MetFrag




2-[2-(Dodecyloxy)ethoxy]ethyl hydrogen sulfate (C12-AE2S)

C16H34O6S [M-H]

10.68

Presence of characteristic fragments m/z: 79.9568 [O3S]; 95.9517 [O4S]; 96.9590 [HO4S], 122.9752 [C2H3O4S]; 167.0019 [C4H7O5S]; 183.1749 [C12H23O] RT is consistent among the analogue series Best match in MetFrag

32 tonnes

Page 34 of 41


2b


2b


2b

16 products

-

2-{2-[2-(Dodecyloxy)ethoxy] ethoxy}ethyl hydrogen sulfate (C12-A·3S)

10.72

Presence of characteristic fragments m/z: 79.9568 [O3S]; 95.9517 [O4S]; 96.9590 [HO4S]; 122.9752 [C2H3O4S]; 167.0019 [C4H7O5S]; 211.0276 [C6H11O6S] RT is consistent among the analogue series Best match in MetFrag


C18H38O7S [M-H]3,6,9,12-Tetraoxatetracos-1-yl hydrogen sulfate (C12-AE4S)

C20H42O8S [M-H]

-

11.05

Presence of characteristic fragments m/z: 79.9568 [O3S]; 95.9517 [O4S]; 96.9590 [HO4S]; 122.9752 [C2H3O4S]; 167.0019 [C4H7O5S]; 183.1749 [C12H23O]; 211.0276 [C6H11O6S]; 255.2329 [C16H31O2] RT is consistent among the analogue series Best match in MetFrag



Page 35 of 41

2-{2-[2-(Tridecyloxy)ethoxy] ethoxy}ethyl hydrogen sulfate (C13-AE3S)


11.3

Presence of characteristic fragments m/z: 79.9568 [O3S]; 96.9590 [HO4S]; 122.9752 [C2H3O4S]; 269.2485 [C17H33O2] Best match in MetFrag

9.9 tonnes


3

• Gear oil

3

47 products

C19H40O7S [M-H]Diethyl hexyl phosphate

10.95

Presence of characteristic fragments m/z: 78.9583 [O3P]; 96.9691 [H2O4P]; 209.0943 [C8H18O4P]

2.5 tonnes

• Sale, maintenance and repair of motor vehicles 20 products

C10H23O4P [M-H]2,2'-Dimorpholinyldiethyl-ether

C12H24N2O3 [M+H]+, [M+Na]+

0.82

Presence of characteristic fragments m/z: 114.0919 [C6H12NO]; 86.0964 [C5H12N]; 158.1181 [C8H16NO2]; 112.0757 [C6H10NO]; 70.0651 [C4H8N]; 84.0801 [C5H10N] CONFIRMED with ref. standard


• • • • •

Sealant Putty Adhesives, solvent based for industrial use Catalysts Insulating materials, heat-cold

• • • • •

Construction of buildings Export Civil engineering Retail sale, except for such with motor vehicles Industry for wood and products of wood


1

14-24


Triisopropanolamine

0.84

Presence of characteristic fragments m/z: 174.1488 [C9H20NO2]; 98.0964 [C6H22N]; 156.1383 [C9H18NO]; 116.1070 [C6H14NO] CONFIRMED with ref. standard


0.86

Presence of characteristic fragments m/z: 144.1388 [C8H18NO]; 88.0762 [C4H10NO]; 106.0868 [C4H12NO2]; 100.1126 [C6H14N] CONFIRMED with ref. standard

1

6-21

• Metalworking and functional fluids (e.g. brake fluid)

1

21-78

• Food additive in beverage, veterinary and pharmaceutical industry

1

5-53

• • • • •

C9H21NO3 [M+H]+ , [M+Na]+ N-Butyldiethanolamine

• Binders for paints, adhesives • Paint, solvent based with anti-corrosive effect for other use • Paint, other water based for industrial use

36 tonnes

Page 36 of 41

Export Paint industry Industry for pulp, paper and paper products Sale, maintenance and repair of motor vehicles Sales establishment for motor vehicle parts and accessories

10 products

C8H19NO2 [M+H]+ , [M+Na]+ Pyridoxine

1.01

Presence of characteristic fragments m/z: 134.0600 [C8H8NO]; 152.0706 [C8H10NO2]; 124.0757 [C7H10NO]; 106.0651 [C7H8N] Similarity with MassBank [record KO003754] CONFIRMED with ref. standard

Not available

C8H11NO3 [M+H]+


Page 37 of 41


Nicotinamide

1.05

Presence of characteristic fragments m/z: 80.0495 [C5H6N]; 108.0444 [C6H6NO]; 106.0287 [C6H4NO] CONFIRMED with ref. standard

0.4 tonnes

• Raw material for cosmetics and hygienic articles • Food and fodder additives

1

32-91

1

1500-3300

1

41-340

7 products

C6H6N2O [M+H]+ Tetraethyleneglycol

1.72 (-0.89)

Presence of characteristic fragments m/z: 133.0855 [C6H13O3]; 89.0594 [C4H9O2]; 103.0387 [C4H7O3] CONFIRMED with ref. standard


C8H23N5 [M+H]+ , [M+Na]+, + [M+NH4] Panthenol

1.75

+

+

C9H19NO4 [M+H] , [M+Na] , [M+NH4]+

Presence of characteristic fragments m/z: 188.1281 [C9H18NO3]; 76.0757 [C3H10NO]; 170.1176 [C9H16NO2]; 102.0549 [C4H8NO2]; 103.0754 [C5H11O2] Similarity with MassBank [record BML01113] CONFIRMED with ref. standard


• • • • •

Hardener Joint-less floors Curing agent for plastics Filling, filler Paint, other curing paint for industrial use

• • • • •

Export Paint industry Surface treatment and coating of metals Specialized construction activities Construction of buildings

• • • •

Vitamins Raw material for cosmetics and hygienic articles Biocides for human hygiene Veterinary pharmaceuticals

• Industry for perfumes and toiletries • Wholesale trade • Retail sale, except for such with motor vehicles • Agriculture



1,2,3-Benzotriazole

3.82

C6H5N3 [M+H]+ N,N-Dimethyl-1-tetradecanamine

12.01

Presence of the fragment m/z: 92.0494 [C6H6N] Plausible MS/MS spectra also in NI Plausible Rt also in NI (1.88) according to the QSRR model Similarity with MassBank [record AU405003] CONFIRMED with ref. standard Presence of characteristic fragments m/z: 228.2691 [C15H34N]; 214.2529 [C14H32N]; 85.1012 [C6H13] CONFIRMED with ref. standard



• Coolants and lubricants for metal processing and metal shearing • Antifreeze • Corrosion inhibitors • Retail sale, except for such with motor vehicles • Industry for fabricated metal products • Industry for pulp, paper and paper products • Export • Industry for motor vehicles • Cleaner • Impregnation agents for textile • Manufacture of textiles

Page 38 of 41

1

160-760

1

12-72

1

17-2200

C16H35N [M+H]+ Tris(2-butoxyethyl) phosphate

12.87 (13.64)

Presence of characteristic fragments m/z: 98.9837 [H4O4P]; 143.0096 [C2H8O5P]; 199.0714 [C6H16O5P]; 299.1607 [C12H28O6P] Similarity with MassBank [record SM880602] CONFIRMED with ref. standard


• • • • •

Waxes and other floor polishes Polish Paint, other water based for industrial use Cleaner Paint, other water based for interior use

• • • • •

Cleaning companies and chimney-sweepers Export Paint industry Wholesale of chemical products Manufacture of textiles

C18H39O7P [M+H]+ , [M+Na]+


Page 39 of 41


Pentaethylene glycol monododecyl ether / Laureth 5

+

14.24

Presence of characteristic fragments m/z: 133.0859 [C6H13O3]; 89.0597 [C4H9O2]; 177.1121 [C8H17O4]; 239.1489 [C10H23O6]; 221.1383 [C10H21O5]; 257.2475 [C16H33O2] CONFIRMED with ref. standard

< 10 tonnes

• Raw material for cosmetics and hygienic articles

1

14-42

1 product

+

C22H46O6 [M+H] , [M+Na] , + [M+NH4] a

Formatted: Dutch (Netherlands)

b

c

Rt = Retention time ; Conc. = Concentration ; ref. standard = reference standard; National Swedish Product Register; Levels of Confidence: 1=Confirmed structure 2a=Probable structure by library 2b=Probable structure by diagnostic evidence 3=Tentative Candidate 4=Unequivocal Molecular Formula 5=Mass of Interest.




Page 40 of 41

Suspect screening and regulatory databases: A ... - ACS Publications

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